Handheld electronic device and method for performing optimized spell checking during text entry by providing a sequentially ordered spell-check algorithms

ABSTRACT

A handheld electronic device includes a reduced QWERTY keyboard and is enabled with a disambiguation routine that is operable to disambiguate text input. In addition to identifying and outputting representations of language objects that are stored in the memory and that correspond with a text input, the device is able to perform a spell check routine during input of a text entry. The spell check routine subjects a text entry to a sequentially ordered series of spell-check algorithms and ceases subjecting the text entry to further spell-check algorithms upon identification of a predetermined quantity of spell-check language objects.

BACKGROUND

1. Field

The disclosed and claimed concept relates generally to handheldelectronic devices and, more particularly, to a handheld electronicdevice having a reduced keyboard and a text input disambiguationfunction that can provide a spell checking feature.

2. Background Information

Numerous types of handheld electronic devices are known. Examples ofsuch handheld electronic devices include, for instance, personal dataassistants (PDAs), handheld computers, two-way pagers, cellulartelephones, and the like. Many handheld electronic devices also featurewireless communication capability, although many such handheldelectronic devices are stand-alone devices that are functional withoutcommunication with other devices.

Such handheld electronic devices are generally intended to be portable,and thus are of a relatively compact configuration in which keys andother input structures often perform multiple functions under certaincircumstances or may otherwise have multiple aspects or featuresassigned thereto. With advances in technology, handheld electronicdevices are built to have progressively smaller form factors yet haveprogressively greater numbers of applications and features residentthereon. As a practical matter, the keys of a keypad can only be reducedto a certain small size before the keys become relatively unusable. Inorder to enable text entry, however, a keypad must be capable ofentering all twenty-six letters of the Latin alphabet, for instance, aswell as appropriate punctuation and other symbols.

One way of providing numerous letters in a small space has been toprovide a “reduced keyboard” in which multiple letters, symbols, and/ordigits, and the like, are assigned to any given key. For example, atouch-tone telephone includes a reduced keypad by providing twelve keys,of which ten have digits thereon, and of these ten keys eight have Latinletters assigned thereto. For instance, one of the keys includes thedigit “2” as well as the letters “A”, “B”, and “C”. Other known reducedkeyboards have included other arrangements of keys, letters, symbols,digits, and the like. Since a single actuation of such a key potentiallycould be intended by the user to refer to any of the letters “A”, “B”,and “C”, and potentially could also be intended to refer to the digit“2”, the input generally is an ambiguous input and is in need of sometype of disambiguation in order to be useful for text entry purposes.

In order to enable a user to make use of the multiple letters, digits,and the like on any given key, numerous keystroke interpretation systemshave been provided. For instance, a “multi-tap” system allows a user tosubstantially unambiguously specify a particular character on a key bypressing the same key a number of times equivalent to the position ofthe desired character on the key. Another exemplary keystrokeinterpretation system would include key chording, of which various typesexist. For instance, a particular character can be entered by pressingtwo keys in succession or by pressing and holding first key whilepressing a second key. Still another exemplary keystroke interpretationsystem would be a “press-and-hold/press-and-release” interpretationfunction in which a given key provides a first result if the key ispressed and immediately released, and provides a second result if thekey is pressed and held for a short period of time. Another keystrokeinterpretation system that has been employed is a software-based textdisambiguation function. In such a system, a user typically presses keysto which one or more characters have been assigned, generally pressingeach key one time for each desired letter, and the disambiguationsoftware attempt to predict the intended input. Numerous such systemshave been proposed, and while many have been generally effective forintended purposes, shortcomings still exist.

For instance, even a single misspelling or mistyping error during textentry on a system employing disambiguation can result in text that bearslittle, if any, resemblance to what was intended by the user. Some spellcheck systems, if employed on a handheld electronic device employingdisambiguation, would provide generally good results, but would alsorequire an enormous amount of processing power, more than typicallywould be available for spell checking on that type of platform. Otherspell check systems, if employed on a handheld electronic deviceemploying disambiguation, would require far less processing power, butwould provide results that are unacceptably poor.

It would be desirable to provide an improved handheld electronic devicewith a reduced keyboard that seeks to mimic a QWERTY keyboard experienceor other particular keyboard experience, and that provides a spellchecking operation that overcomes the shortcomings of disambiguationsystems. Such an improved handheld electronic device might alsodesirably be configured with enough features to enable text entry andother tasks with relative ease.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding of the disclosed and claimed concept can be gainedfrom the following Description when read in conjunction with theaccompanying drawings in which:

FIG. 1 is a top plan view of an improved handheld electronic device inaccordance with the disclosed and claimed concept;

FIG. 2 is a schematic depiction of the improved handheld electronicdevice of FIG. 1;

FIG. 2A is a schematic depiction of a portion of the handheld electronicdevice of FIG. 2;

FIGS. 3A, 3B, and 3C are an exemplary flowchart depicting certainaspects of a disambiguation function that can be executed on thehandheld electronic device of FIG. 1;

FIG. 4 is another exemplary flowchart depicting certain aspects of alearning method that can be executed on the handheld electronic device;

FIG. 5 is an exemplary output during a text entry operation;

FIG. 6 is another exemplary output during another part of the text entryoperation;

FIG. 7 is another exemplary output during another part of the text entryoperation;

FIG. 8 is another exemplary output during another part of the text entryoperation;

FIGS. 9A and 9B are an exemplary flowchart depicting a spell checkingoperation during a text entry operation;

FIG. 10 is another exemplary output during another part of the textentry operation;

FIG. 11 is another exemplary output during another part of the textentry operation;

FIG. 12 is another exemplary output during another part of the textentry operation.

Similar numerals refer to similar parts throughout the specification.

DESCRIPTION

An improved handheld electronic device 4 is indicated generally in FIG.1 and is depicted schematically in FIG. 2. The exemplary handheldelectronic device 4 includes a housing 6 upon which are disposed aprocessor unit that includes an input apparatus 8, an output apparatus12, a processor 16, a memory 20, and at least a first routine. Theprocessor 16 may be, for instance, and without limitation, amicroprocessor (μP) and is responsive to inputs from the input apparatus8 and provides output signals to the output apparatus 12. The processor16 also interfaces with the memory 20. The processor 16 and the memory20 together form a processor apparatus. Examples of handheld electronicdevices are included in U.S. Pat. Nos. 6,452,588 and 6,489,950.

As can be understood from FIG. 1, the input apparatus 8 includes akeypad 24 and a thumbwheel 32. As will be described in greater detailbelow, the keypad 24 is in the exemplary form of a reduced QWERTYkeyboard including a plurality of keys 28 that serve as input members.It is noted, however, that the keypad 24 may be of other configurations,such as an AZERTY keyboard, a QWERTZ keyboard, or other keyboardarrangement, whether presently known or unknown, and either reduced ornot reduced. As employed herein, the expression “reduced” and variationsthereof in the context of a keyboard, a keypad, or other arrangement ofinput members, shall refer broadly to an arrangement in which at leastone of the input members has assigned thereto a plurality of linguisticelements such as, for example, characters in the set of Latin letters,whereby an actuation of the at least one of the input members, withoutanother input in combination therewith, is an ambiguous input since itcould refer to more than one of the plurality of linguistic elementsassigned thereto. As employed herein, the expression “linguisticelement” and variations thereof shall refer broadly to any element thatitself can be a language object or from which a language object can beconstructed, identified, or otherwise obtained, and thus would include,for example and without limitation, characters, letters, strokes,ideograms, phonemes, morphemes, digits, and the like. As employedherein, the expression “language object” and variations thereof shallrefer broadly to any type of object that may be constructed, identified,or otherwise obtained from one or more linguistic elements, that can beused alone or in combination to generate text, and that would include,for example and without limitation, words, shortcuts, symbols,ideograms, and the like.

The system architecture of the handheld electronic device 4advantageously is organized to be operable independent of the specificlayout of the keypad 24. Accordingly, the system architecture of thehandheld electronic device 4 can be employed in conjunction withvirtually any keypad layout substantially without requiring anymeaningful change in the system architecture. It is further noted thatcertain of the features set forth herein are usable on either or both ofa reduced keyboard and a non-reduced keyboard.

The keys 28 are disposed on a front face of the housing 6, and thethumbwheel 32 is disposed at a side of the housing 6. The thumbwheel 32can serve as another input member and is both rotatable, as is indicatedby the arrow 34, to provide selection inputs to the processor 16, andalso can be pressed in a direction generally toward the housing 6, as isindicated by the arrow 38, to provide another selection input to theprocessor 16.

As can further be seen in FIG. 1, many of the keys 28 include a numberof linguistic elements 48 disposed thereon. As employed herein, theexpression “a number of” and variations thereof shall refer broadly toany quantity, including a quantity of one. In the exemplary depiction ofthe keypad 24, many of the keys 28 include two linguistic elements, suchas including a first linguistic element 52 and a second linguisticelement 56 assigned thereto.

One of the keys 28 of the keypad 24 includes as the characters 48thereof the letters “Q” and “W”, and an adjacent key 28 includes as thecharacters 48 thereof the letters “E” and “R”. It can be seen that thearrangement of the characters 48 on the keys 28 of the keypad 24 isgenerally of a QWERTY arrangement, albeit with many of the keys 28including two of the characters 48.

The output apparatus 12 includes a display 60 upon which can be providedan output 64. An exemplary output 64 is depicted on the display 60 inFIG. 1. The output 64 includes a text component 68 and a variantcomponent 72. The variant component 72 includes a default portion 76 anda variant portion 80. The display also includes a caret 84 that depictsgenerally where the next input from the input apparatus 8 will bereceived.

The text component 68 of the output 64 provides a depiction of thedefault portion 76 of the output 64 at a location on the display 60where the text is being input. The variant component 72 is disposedgenerally in the vicinity of the text component 68 and provides, inaddition to the default proposed output 76, a depiction of the variousalternate text choices, i.e., alternates to the default proposed output76, that are proposed by an input disambiguation function in response toan input sequence of key actuations of the keys 28.

As will be described in greater detail below, the default portion 76 isproposed by the disambiguation function as being the most likelydisambiguated interpretation of the ambiguous input provided by theuser. The variant portion 80 includes a predetermined quantity ofalternate proposed interpretations of the same ambiguous input fromwhich the user can select, if desired. It is noted that the exemplaryvariant portion 80 is depicted herein as extending vertically below thedefault portion 76, but it is understood that numerous otherarrangements could be provided.

The memory 20 is depicted schematically in FIG. 2A. The memory 20 can beany of a variety of types of internal and/or external storage media suchas, without limitation, RAM, ROM, EPROM(s), EEPROM(s), and the like thatprovide a storage register for data storage such as in the fashion of aninternal storage area of a computer, and can be volatile memory ornonvolatile memory. The memory 20 additionally includes a number ofroutines depicted generally with the numeral 22 for the processing ofdata. The routines 22 can be in any of a variety of forms such as,without limitation, software, firmware, and the like. As will beexplained in greater detail below, the routines 22 include theaforementioned disambiguation function as an application, spell checkroutines, and other routines.

As can be understood from FIG. 2A, the memory 20 additionally includesdata stored and/or organized in a number of tables, sets, lists, and/orotherwise. Specifically, the memory 20 includes a generic word list 88,a new words database 92, another data source 99 and a dynamic autotexttable 49.

Stored within the various areas of the memory 20 are a number oflanguage objects 100 and frequency objects 104. The language objects 100generally are each associated with an associated frequency object 104.The language objects 100 include, in the present exemplary embodiment, aplurality of word objects 108 and a plurality of N-gram objects 112. Theword objects 108 are generally representative of complete words withinthe language or custom words stored in the memory 20. For instance, ifthe language stored in the memory 20 is, for example, English, generallyeach word object 108 would represent a word in the English language orwould represent a custom word.

Associated with substantially each word object 108 is a frequency object104 having frequency value that is indicative of the relative frequencywithin the relevant language of the given word represented by the wordobject 108. In this regard, the generic word list 88 includes aplurality of word objects 108 and associated frequency objects 104 thattogether are representative of a wide variety of words and theirrelative frequency within a given vernacular of, for instance, a givenlanguage. The generic word list 88 can be derived in any of a widevariety of fashions, such as by analyzing numerous texts and otherlanguage sources to determine the various words within the languagesources as well as their relative probabilities, i.e., relativefrequencies, of occurrences of the various words within the languagesources.

The N-gram objects 112 stored within the generic word list 88 are shortstrings of characters within the relevant language typically, forexample, one to three characters in length, and typically represent wordfragments within the relevant language, although certain of the N-gramobjects 112 additionally can themselves be words. However, to the extentthat an N-gram object 112 also is a word within the relevant language,the same word likely would be separately stored as a word object 108within the generic word list 88. As employed herein, the expression“string” and variations thereof shall refer broadly to an object havingone or more characters or components, and can refer to any of a completeword, a fragment of a word, a custom word or expression, and the like.

In the present exemplary embodiment of the handheld electronic device 4,the N-gram objects 112 include 1-gram objects, i.e., string objects thatare one character in length, 2-gram objects, i.e., string objects thatare two characters in length, and 3-gram objects, i.e., string objectsthat are three characters in length, all of which are collectivelyreferred to as N-grams 112. Substantially each N-gram object 112 in thegeneric word list 88 is similarly associated with an associatedfrequency object 104 stored within the generic word list 88, but thefrequency object 104 associated with a given N-gram object 112 has afrequency value that indicates the relative probability that thecharacter string represented by the particular N-gram object 112 existsat any location within any word of the relevant language. The N-gramobjects 112 and the associated frequency objects 104 are a part of thecorpus of the generic word list 88 and are obtained in a fashion similarto the way in which the word object 108 and the associated frequencyobjects 104 are obtained, although the analysis performed in obtainingthe N-gram objects 112 will be slightly different because it willinvolve analysis of the various character strings within the variouswords instead of relying primarily on the relative occurrence of a givenword.

The present exemplary embodiment of the handheld electronic device 4,with its exemplary language being the English language, includestwenty-six 1-gram N-gram objects 112, i.e., one 1-gram object for eachof the twenty-six letters in the Latin alphabet upon which the Englishlanguage is based, and further includes 676 2-gram N-gram objects 112,i.e., twenty-six squared, representing each two-letter permutation ofthe twenty-six letters within the Latin alphabet.

The N-gram objects 112 also include a certain quantity of 3-gram N-gramobjects 112, primarily those that have a relatively high frequencywithin the relevant language. The exemplary embodiment of the handheldelectronic device 4 includes fewer than all of the three-letterpermutations of the twenty-six letters of the Latin alphabet due toconsiderations of data storage size, and also because the 2-gram N-gramobjects 112 can already provide a meaningful amount of informationregarding the relevant language. As will be set forth in greater detailbelow, the N-gram objects 112 and their associated frequency objects 104provide frequency data that can be attributed to character strings forwhich a corresponding word object 108 cannot be identified or has notbeen identified, and typically is employed as a fallback data source,although this need not be exclusively the case.

In the present exemplary embodiment, the language objects 100 and thefrequency objects 104 are maintained substantially inviolate in thegeneric word list 88, meaning that the basic language dictionary remainssubstantially unaltered within the generic word list 88, and thelearning functions that are provided by the handheld electronic device 4and that are described below operate in conjunction with other objectthat are generally stored elsewhere in memory 20, such as, for example,in the new words database 92.

The new words database 92 stores additional word objects 108 andassociated frequency objects 104 in order to provide to a user acustomized experience in which words and the like that are usedrelatively more frequently by a user will be associated with relativelyhigher frequency values than might otherwise be reflected in the genericword list 88. More particularly, the new words database 92 includes wordobjects 108 that are user-defined and that generally are not found amongthe word objects 108 of the generic word list 88. Each word object 108in the new words database 92 has associated therewith an associatedfrequency object 104 that is also stored in the new words database 92.

FIGS. 3A, 3B, and 3C depict in an exemplary fashion the generaloperation of certain aspects of the disambiguation function of thehandheld electronic device 4. Additional features, functions, and thelike are depicted and described elsewhere.

An input is detected, as at 204, and the input can be any type ofactuation or other operation as to any portion of the input apparatus 8.A typical input would include, for instance, an actuation of a key 28having a number of characters 48 thereon, or any other type of actuationor manipulation of the input apparatus 8.

The disambiguation function then determines, as at 212, whether thecurrent input is an operational input, such as a selection input, adelimiter input, a movement input, an alternation input, or, forinstance, any other input that does not constitute an actuation of a key28 having a number of characters 48 thereon. If the input is determinedat 212 to not be an operational input, processing continues at 216 byadding the input to the current input sequence which may or may notalready include an input.

Many of the inputs detected at 204 are employed in generating inputsequences as to which the disambiguation function will be executed. Aninput sequence is build up in each “session”—with each actuation of akey 28 having a number of characters 48 thereon. Since an input sequencetypically will be made up of at least one actuation of a key 28 having aplurality of characters 48 thereon, the input sequence will beambiguous. When a word, for example, is completed the current session isended an a new session is initiated.

An input sequence is gradually built up on the handheld electronicdevice 4 with each successive actuation of a key 28 during any givensession. Specifically, once a delimiter input is detected during anygiven session, the session is terminated and a new session is initiated.Each input resulting from an actuation of one of the keys 28 having anumber of the characters 48 associated therewith is sequentially addedto the current input sequence. As the input sequence grows during agiven session, the disambiguation function generally is executed witheach actuation of a key 28, i.e., input, and as to the entire inputsequence. Stated otherwise, within a given session, the growing inputsequence is attempted to be disambiguated as a unit by thedisambiguation function with each successive actuation of the variouskeys 28.

Once a current input representing a most recent actuation of the one ofthe keys 28 having a number of the characters 48 assigned thereto hasbeen added to the current input sequence within the current session, asat 216 in FIG. 3A, the disambiguation function generates, as at 220,substantially all of the permutations of the characters 48 assigned tothe various keys 28 that were actuated in generating the input sequence.In this regard, the “permutations” refer to the various strings that canresult from the characters 48 of each actuated key 28 limited by theorder in which the keys 28 were actuated. The various permutations ofthe characters in the input sequence are employed as prefix objects.

For instance, if the current input sequence within the current sessionis the ambiguous input of the keys “AS” and “OP”, the variouspermutations of the first character 52 and the second character 56 ofeach of the two keys 28, when considered in the sequence in which thekeys 28 were actuated, would be “SO”, “SP”, “AP”, and “AO”, and each ofthese is a prefix object that is generated, as at 220, with respect tothe current input sequence. As will be explained in greater detailbelow, the disambiguation function seeks to identify for each prefixobject one of the word objects 108 for which the prefix object would bea prefix.

For each generated prefix object, the memory 20 is consulted, as at 224,to identify, if possible, for each prefix object one of the word objects108 in the memory 20 that corresponds with the prefix object, meaningthat the sequence of letters represented by the prefix object would beeither a prefix of the identified word object 108 or would besubstantially identical to the entirety of the word object 108. Furtherin this regard, the word object 108 that is sought to be identified isthe highest frequency word object 108. That is, the disambiguationfunction seeks to identify the word object 108 that corresponds with theprefix object and that also is associated with a frequency object 104having a relatively higher frequency value than any of the otherfrequency objects 104 associated with the other word objects 108 thatcorrespond with the prefix object.

It is noted in this regard that the word objects 108 in the generic wordlist 88 are generally organized in data tables that correspond with thefirst two letters of various words. For instance, the data tableassociated with the prefix “CO” would include all of the words such as“CODE”, “COIN”, “COMMUNICATION”, and the like. Depending upon thequantity of word objects 108 within any given data table, the data tablemay additionally include sub-data tables within which word objects 108are organized by prefixes that are three characters or more in length.Continuing onward with the foregoing example, if the “CO” data tableincluded, for instance, more than 256 word objects 108, the “CO” datatable would additionally include one or more sub-data tables of wordobjects 108 corresponding with the most frequently appearingthree-letter prefixes. By way of example, therefore, the “CO” data tablemay also include a “COM” sub-data table and a “CON” sub-data table. If asub-data table includes more than the predetermined number of wordobjects 108, for example a quantity of 256, the sub-data table mayinclude further sub-data tables, such as might be organized according toa four letter prefixes. It is noted that the aforementioned quantity of256 of the word objects 108 corresponds with the greatest numericalvalue that can be stored within one byte of the memory 20.

Accordingly, when, at 224, each prefix object is sought to be used toidentify a corresponding word object 108, and for instance the instantprefix object is “AP”, the “AP” data table will be consulted. Since allof the word objects 108 in the “AP” data table will correspond with theprefix object “AP”, the word object 108 in the “AP” data table withwhich is associated a frequency object 104 having a frequency valuerelatively higher than any of the other frequency objects 104 in the“AP” data table is identified. The identified word object 108 and theassociated frequency object 104 are then stored in a result registerthat serves as a result of the various comparisons of the generatedprefix objects with the contents of the memory 20.

It is noted that one or more, or possibly all, of the prefix objectswill be prefix objects for which a corresponding word object 108 is notidentified in the memory 20. Such prefix objects are considered to beorphan prefix objects and are separately stored or are otherwiseretained for possible future use. In this regard, it is noted that manyor all of the prefix objects can become orphan object if, for instance,the user is trying to enter a new word or, for example, if the user hasmis-keyed and no word corresponds with the mis-keyed input.

Processing thereafter continues, as at 226, where it is determinedwhether nor not any language objects 100 were identified at 224. If itis determined at 226 that no language objects were identified at 224,processing continues, as at 230, which sends processing to a spellchecking operation depicted generally in FIG. 12, and which will bedescribed in greater detail below.

If, however, it is determined at 226 that one or more language objects100 were identified at 224, processing continues, as at 232 in FIG. 3C,where duplicate word objects 108 associated with relatively lowerfrequency values are deleted from the result. Such a duplicate wordobject 108 could be generated, for instance, by the other data source99.

Once the duplicate word objects 108 and the associated frequency objects104 have been removed at 232, processing continues, as at 236, whereinthe remaining prefix objects are arranged in an output set in decreasingorder of frequency value.

If it is determined, as at 240, that the flag has been set, meaning thata user has made a selection input, either through an express selectioninput or through an alternation input of a movement input, then thedefault output 76 is considered to be “locked,” meaning that theselected variant will be the default prefix until the end of thesession. If it is determined at 240 that the flag has been set, theprocessing will proceed to 244 where the contents of the output set willbe altered, if needed, to provide as the default output 76 an outputthat includes the selected prefix object, whether it corresponds with aword object 108 or is an artificial variant. In this regard, it isunderstood that the flag can be set additional times during a session,in which case the selected prefix associated with resetting of the flagthereafter becomes the “locked” default output 76 until the end of thesession or until another selection input is detected.

Processing then continues, as at 248, to an output step after which anoutput 64 is generated as described above. Processing thereaftercontinues at 204 where additional input is detected. On the other hand,if it is determined at 240 that the flag had not been set, thenprocessing goes directly to 248 without the alteration of the contentsof the output set at 244.

If the detected input is determined, as at 212, to be an operationalinput, processing then continues to determine the specific nature of theoperational input. For instance, if it is determined, as at 252, thatthe current input is a selection input, processing continues at 254where the flag is set. Processing then returns to detection ofadditional inputs as at 204.

If it is determined, as at 260, that the input is a delimiter input,processing continues at 264 where the current session is terminated andprocessing is transferred, as at 266, to the learning functionsubsystem, as at 404 of FIG. 4. A delimiter input would include, forexample, the actuation of a <SPACE> key 116, which would both enter adelimiter symbol and would add a space at the end of the word, actuationof the <ENTER> key, which might similarly enter a delimiter input andenter a space, and by a translation of the thumbwheel 32, such as isindicated by the arrow 38, which might enter a delimiter input withoutadditionally entering a space.

It is first determined, as at 408, whether the default output at thetime of the detection of the delimiter input at 260 matches a wordobject 108 in the memory 20. If it does not, this means that the defaultoutput is a user-created output that should be added to the new wordsdatabase 92 for future use. In such a circumstance processing thenproceeds to 412 where the default output is stored in the new wordsdatabase 92 as a new word object 108. Additionally, a frequency object104 is stored in the new words database 92 and is associated with theaforementioned new word object 108. The new frequency object 104 isgiven a relatively high frequency value, typically within the upperone-fourth or one-third of a predetermined range of possible frequencyvalues.

In this regard, frequency objects 104 are given an absolute frequencyvalue generally in the range of zero to 65,535. The maximum valuerepresents the largest number that can be stored within two bytes of thememory 20. The new frequency object 104 that is stored in the new wordsdatabase 92 is assigned an absolute frequency value within the upperone-fourth or one-third of this range, particularly since the new wordwas used by a user and is likely to be used again.

With further regard to frequency object 104, it is noted that within agiven data table, such as the “CO” data table mentioned above, theabsolute frequency value is stored only for the frequency object 104having the highest frequency value within the data table. All of theother frequency objects 104 in the same data table have frequency valuesstored as percentage values normalized to the aforementioned maximumabsolute frequency value. That is, after identification of the frequencyobject 104 having the highest frequency value within a given data table,all of the other frequency objects 104 in the same data table areassigned a percentage of the absolute maximum value, which representsthe ratio of the relatively smaller absolute frequency value of aparticular frequency object 104 to the absolute frequency value of theaforementioned highest value frequency object 104. Advantageously, suchpercentage values can be stored within a single byte of memory, thussaving storage space within the handheld electronic device 4.

Upon creation of the new word object 108 and the new frequency object104, and storage thereof within the new words database 92, processing istransferred to 420 where the learning process is terminated. Processingis then returned to the main process, as at 204. If at 408 it isdetermined that the word object 108 in the default output 76 matches aword object 108 within the memory 20, processing is returned directly tothe main process at 204.

With further regard to the identification of various word objects 108for correspondence with generated prefix objects, it is noted that thememory 20 can include a number of additional data sources 99 in additionto the generic word list 88 and the new words database 92, all of whichcan be considered linguistic sources. It is understood that the memory20 might include any number of other data sources 99. The other datasources 99 might include, for example, an address database, a speed-textdatabase, or any other data source without limitation. An exemplaryspeed-text database might include, for example, sets of words orexpressions or other data that are each associated with, for example, acharacter string that may be abbreviated. For example, a speed-textdatabase might associate the string “br” with the set of words “BestRegards”, with the intention that a user can type the string “br” andreceive the output “Best Regards”.

In seeking to identify word objects 108 that correspond with a givenprefix object, the handheld electronic device 4 may poll all of the datasources in the memory 20. For instance the handheld electronic device 4may poll the generic word list 88, the new words database 92, the otherdata sources 99, and the dynamic autotext table 49 to identify wordobjects 108 that correspond with the prefix object. The contents of theother data sources 99 may be treated as word objects 108, and theprocessor 16 may generate frequency objects 104 that will be associatedwith such word objects 108 and to which may be assigned a frequencyvalue in, for example, the upper one-third or one-fourth of theaforementioned frequency range. Assuming that the assigned frequencyvalue is sufficiently high, the string “br”, for example, wouldtypically be output to the display 60. If a delimiter input is detectedwith respect to the portion of the output having the association withthe word object 108 in the speed-text database, for instance “br”, theuser would receive the output “Best Regards”, it being understood thatthe user might also have entered a selection input as to the exemplarystring “br”.

The contents of any of the other data sources 99 may be treated as wordobjects 108 and may be associated with generated frequency objects 104having the assigned frequency value in the aforementioned upper portionof the frequency range. After such word objects 108 are identified, thenew word learning function can, if appropriate, act upon such wordobjects 108 in the fashion set forth above.

If it is determined, such as at 268, that the current input is amovement input, such as would be employed when a user is seeking to editan object, either a completed word or a prefix object within the currentsession, the caret 84 is moved, as at 272, to the desired location, andthe flag is set, as at 276. Processing then returns to where additionalinputs can be detected, as at 204.

In this regard, it is understood that various types of movement inputscan be detected from the input device 8. For instance, a rotation of thethumbwheel 32, such as is indicated by the arrow 34 of FIG. 1, couldprovide a movement input. In the instance where such a movement input isdetected, such as in the circumstance of an editing input, the movementinput is additionally detected as a selection input. Accordingly, and asis the case with a selection input such as is detected at 252, theselected variant is effectively locked with respect to the defaultportion 76 of the output 64. Any default output 76 during the samesession will necessarily include the previously selected variant.

In the present exemplary embodiment of the handheld electronic device 4,if it is determined, as at 252, that the input is not a selection input,and it is determined, as at 260, that the input is not a delimiterinput, and it is further determined, as at 268, that the input is not amovement input, in the current exemplary embodiment of the handheldelectronic device 4 the only remaining operational input generally is adetection of the <DELETE> key 86 of the keys 28 of the keypad 24. Upondetection of the <DELETE> key 86, the final character of the defaultoutput is deleted, as at 280. Processing thereafter returns to 204 whereadditional input can be detected.

An exemplary input sequence is depicted in FIGS. 1 and 5-8. In thisexample, the user is attempting to enter the word “APPLOADER”, and thisword presently is not stored in the memory 20. In FIG. 1 the user hasalready typed the “AS” key 28. Since the data tables in the memory 20are organized according to two-letter prefixes, the contents of theoutput 64 upon the first keystroke are obtained from the N-gram objects112 within the memory. The first keystroke “AS” corresponds with a firstN-gram object 112 “S” and an associated frequency object 104, as well asanother N-gram object 112 “A” and an associated frequency object 104.While the frequency object 104 associated with “S” has a frequency valuegreater than that of the frequency object 104 associated with “A”, it isnoted that “A” is itself a complete word. A complete word is alwaysprovided as the default output 76 in favor of other prefix objects thatdo not match complete words, regardless of associated frequency value.As such, in FIG. 1, the default portion 76 of the output 64 is “A”.

In FIG. 5, the user has additionally entered the “OP” key 28. Thevariants are depicted in FIG. 5. Since the prefix object “SO” is also aword, it is provided as the default output 76. In FIG. 6, the user hasagain entered the “OP” key 28 and has also entered the “L” key 28. It isnoted that the exemplary “L” key 28 depicted herein includes only thesingle character 48 “L”.

It is assumed in the instant example that no operational inputs havethus far been detected. The default output 76 is “APPL”, such as wouldcorrespond with the word “APPLE”. The prefix “APPL” is depicted both inthe text component 68, as well as in the default portion 76 of thevariant component 72. Variant prefix objects in the variant portion 80include “APOL”, such as would correspond with the word “APOLOGIZE”, andthe prefix “SPOL”, such as would correspond with the word “SPOLIATION”.

It is particularly noted that the additional variants “AOOL”, “AOPL”,“SOPL”, and “SOOL” are also depicted as variants 80 in the variantcomponent 72. Since no word object 108 corresponds with these prefixobjects, the prefix objects are considered to be orphan prefix objectsfor which a corresponding word object 108 was not identified In thisregard, it may be desirable for the variant component 72 to include aspecific quantity of entries, and in the case of the instant exemplaryembodiment the quantity is seven entries. Upon obtaining the result at224, if the quantity of prefix objects in the result is fewer than thepredetermined quantity, the disambiguation function will seek to provideadditional outputs until the predetermined number of outputs areprovided.

In FIG. 7 the user has additionally entered the “OP” key 28. In thiscircumstance, and as can be seen in FIG. 7, the default portion 76 ofthe output 64 has become the prefix object “APOLO” such as wouldcorrespond with the word “APOLOGIZE”, whereas immediately prior to thecurrent input the default portion 76 of the output 64 of FIG. 6 was“APPL” such as would correspond with the word “APPLE.” Again, assumingthat no operational inputs had been detected, the default prefix objectin FIG. 7 does not correspond with the previous default prefix object ofFIG. 6. As such, a first artificial variant “APOLP” is generated and inthe current example is given a preferred position. The aforementionedartificial variant “APOLP” is generated by deleting the final characterof the default prefix object “APOLO” and by supplying in its place anopposite character 48 of the key 28 which generated the final characterof the default portion 76 of the output 64, which in the current exampleof FIG. 7 is “P”, so that the aforementioned artificial variant is“APOLP”.

Furthermore, since the previous default output “APPL” corresponded witha word object 108, such as the word object 108 corresponding with theword “APPLE”, and since with the addition of the current input theprevious default output “APPL” no longer corresponds with a word object108, two additional artificial variants are generated. One artificialvariant is “APPLP” and the other artificial variant is “APPLO”, andthese correspond with the previous default output “APPL” plus thecharacters 48 of the key 28 that was actuated to generate the currentinput. These artificial variants are similarly output as part of thevariant portion 80 of the output 64.

As can be seen in FIG. 7, the default portion 76 of the output 64“APOLO” no longer seems to match what would be needed as a prefix for“APPLOADER”, and the user likely anticipates that the desired word“APPLOADER” is not already stored in the memory 20. As such, the userprovides a selection input, such as by scrolling with the thumbwheel 32until the variant string “APPLO” is highlighted. The user then continuestyping and enters the “AS” key.

The output 64 of such action is depicted in FIG. 8. Here, the string“APPLOA” is the default portion 76 of the output 64. Since the variantstring “APPLO” became the default portion 76 of the output 64 (notexpressly depicted herein) as a result of the selection input as to thevariant string “APPLO”, and since the variant string “APPLO” does notcorrespond with a word object 108, the character strings “APPLOA” and“APPLOS” were created as artificial variants. Additionally, since theprevious default of FIG. 7, “APOLO” previously had corresponded with aword object 108, but now is no longer in correspondence with the defaultportion 76 of the output 64 of FIG. 8, the additional artificialvariants of “APOLOA” and “APOLOS” were also generated. Such artificialvariants are given a preferred position in favor of the three displayedorphan prefix objects.

Since the current input sequence in the example no longer correspondswith any word object 108, the portions of the method related toattempting to find corresponding word objects 108 are not executed withfurther inputs for the current session. That is, since no word object108 corresponds with the current input sequence, further inputs willlikewise not correspond with any word object 108. Avoiding the search ofthe memory 20 for such nonexistent word objects 108 saves time andavoids wasted processing effort.

As the user continues to type, the user ultimately will successfullyenter the word “APPLOADER” and will enter a delimiter input. Upondetection of the delimiter input after the entry of “APPLOADER”, thelearning function is initiated. Since the word “APPLOADER” does notcorrespond with a word object 108 in the memory 20, a new word object108 corresponding with “APPLOADER” is generated and is stored in the newwords database 92, along with a corresponding new frequency object 104which is given an absolute frequency in the upper, say, one-third orone-fourth of the possible frequency range. In this regard, it is notedthat the new words database 92 is generally organized in two-characterprefix data tables similar to those found in the generic word list 88.As such, the new frequency object 104 is initially assigned an absolutefrequency value, but upon storage the absolute frequency value, if it isnot the maximum value within that data table, will be changed to includea normalized frequency value percentage normalized to whatever is themaximum frequency value within that data table.

It is noted that the layout of the characters 48 disposed on the keys 28in FIG. 1 is an exemplary character layout that would be employed wherethe intended primary language used on the handheld electronic device 4was, for instance, English. Other layouts involving these characters 48and/or other characters can be used depending upon the intended primarylanguage and any language bias in the makeup of the language objects100.

As mentioned elsewhere herein, if it is determined at 226 that nolanguage objects 100 were identified at 224 as corresponding with theprefix objects, processing transfers, as at 230 in FIG. 3A, to the spellchecking routine depicted generally in FIGS. 9A and 9B. As a generalmatter, the spell checking routine of the disclosed and claimed conceptadvantageously provides a series of sequentially ordered spell-checkalgorithms to which a text entry is subjected. Once a predeterminednumber of spell-check language objects 100 have been identified, such asthrough processing with the spell-check algorithms, further subjectingof the text entry to additional spell-check algorithms is ceased. In theexemplary embodiment described herein, the spell checking operation isperformed on the various orphan prefix objects, i.e., the prefix objectsfor which no corresponding language object 100 was identified. It isfurther noted that certain of the orphan prefix objects might beartificial variants generated as described herein. It is understood,however, that the text entry that could be subjected to the disclosedand claimed process could be, for instance and without limitation, akeystroke sequence, a series of other linguistic elements, and the like.

Advantageously, the spell-check method is executed during entry of text,rather than waiting until a given text entry has been finalized. Thatis, the spell-check method of the disclosed and claimed concept is beingexecuted during any given session on the handheld electronic device 4and prior to detection of a delimiter input. As such, the user can bequickly apprised of the existence of a possible spelling error prior tofully keying a text entry, which facilitates correct text entry. In thisregard, it is noted that spell check results are output as a generalmatter at a position of relatively lower priority than artificialvariants. That is, the entry of new words is to be encouraged, and theentry of new words often accompanies the output of one or moreartificial variants.

It is further noted, however, that the spell-check routine of thedisclosed and claimed concept additionally can provide a learningfunction that can learn the various spelling errors that the particularuser of the handheld electronic device typically makes and corrects. Inthe event such a learned spelling error is again entered by the user,the correctly spelled word reflected in the dynamic autotext table 49 isoutput as a default output, i.e., at a position of relative prioritywith respect to the artificial variants that are also output.

The spell-check algorithms are sequentially arranged in a specificorder, meaning that a text entry is first processed according to a firstspell-check algorithm and, if the identified spell-check languageobjects 100 do not reach a predetermined quantity, the text entry isprocessed according to a second spell-check algorithm. If the identifiedspell-check language objects 100 still do not reach the predeterminedquantity, the text entry is processed according to a third spell-checkalgorithm, and so forth.

The spell-check algorithms, being sequentially ordered, can further begrouped as follows: A text entry will first be subjected to one or morespell-check algorithms related to character configuration which, in thepresent exemplary embodiment, is a spell-check algorithm that is relatedto ignoring capitalization and accenting. If the identified spell-checklanguage objects 100 do not reach the predetermined quantity, the textentry is thereafter subjected to one or more spell-check algorithmsrelated to misspelling which, in the present exemplary embodiment, is aspell-check algorithm that is related to phonetic replacement. If theidentified spell-check language objects 100 do not reach thepredetermined quantity, the text entry is thereafter subjected to one ormore spell-check algorithms related to mistyping. In this regard,“misspelling” generally refers to a mistake by the user as to how aparticular word, for instance, is spelled, such as if the userincorrectly believed that the word—their—was actually spelled “their”.In contrast, “mistyping” generally refers to a keying error by the user,such as if the user keyed an entry other than what was desired.

If the identified spell-check language objects 100 do not reach thepredetermined quantity, the text entry is thereafter subjected to one ormore spell-check algorithms that are related to specific affixationrules, which typically are locale specific. For instance, in the Germanlanguage two known words are kapitan and patent. These two words can becombined into a single expression, but in order to do so an s must beaffixed between the two, thus kapitanpatent. Other types of affixationrules will be apparent.

If the identified spell-check language objects 100 do not reach thepredetermined quantity, the text entry is thereafter subjected to one ormore spell-check algorithms related to metaphone analysis. As a generalmatter, a metaphone is a phonetic algorithm for indexing words by theirsound. Both metaphone and phonetic rules are language-specific.Metaphones thus enable a linguistic expression to be characterized in astandardized fashion that is somewhat phonetic in nature. The use ofmetaphones can help to overcome certain misspelling errors.

To more specifically describe the process, a given text entry such as astring of characters is subjected to a given spell-check algorithm,which results in the generation of an expression. For instance, thespell check algorithm might be directed toward replacing a givencharacter string with a phonetic replacement. The resultant “expression”thus would be a characterization of the text entry as processed by thealgorithm. For instance, the character string “ph” might be phoneticallyreplaced by “f” and/or “gh”. The language sources in the memory 20 wouldthen be consulted to see if any language objects 100 corresponding withthe text input incorporating the phonetic replacements can beidentified.

It is noted, however, that such a description is conceptual only, andthat such processed or “resultant” character strings often are notsearched individually. Rather, the result of subjecting a text entry toa spell-check algorithm can many times result in a “regular expression”which is a global characterization of the processed text entry. Forinstance, a “regular expression” would contain wild card charactersthat, in effect, characterize the result of all of the possiblepermutations of the text entry according to the particular spell-checkalgorithm. The result is that generally a single search can be performedon a “regular expression”, with consequent savings in processingcapacity and efficiency.

By way of example, if the user entered <OP><GH< ><AS><BN>, such as mightspell --phan--, the processing of --phan-- according to the exemplaryphonetic replacement spell-check algorithm would result in the regularexpression characterized as {f|v|ph|gh|}{a|ei|ey}n, by way of example.The “ph” can be phonetically replaced by any of “f”, “v”, “ph”, and“gh”, and the “a” can be replaced by and of “a”, “ei”, and “ey”. The “n”does not have any phonetic equivalent. The generic word list 88, the newwords database 92, the other data sources 99, and the dynamic autotexttable 49 would be checked to see if any language object 100 could beidentified as being consistent with the expression{f|v|ph|gh|}{a|ei|ey}n. Any such identified language object 100 would beconsidered a spell-check language object 100. If, after such searchingof the linguistic sources, the quantity of identified spell-checklanguage objects 100 does not reach the predetermined quantity, the textentry --phan--, for example, would then be subjected to the sequentiallynext spell-check algorithm, which would result in the generation of adifferent regular expression or of other processed strings, which wouldthen be the subject of one or more new searches of the linguistic datasources for language objects 100 that are consistent therewith.

As mentioned above, the first spell-check algorithm is one that ignorescapitalization and/or accenting. The ignoring of capitalization and/oraccenting can be performed with respect to capitalization and/oraccenting that is contained in the text entry which is the subject ofthe search and/or that is contained in the stored language objects 100being searched.

The sequentially next spell-check algorithm is the aforementionedphonetic replacement algorithm. Certain character strings are replaced,i.e., in a regular expression, to identify language objects 100 that arephonetically similar to the text entry. Some exemplary phoneticreplacements are listed in Table 1.

TABLE 1 Exemplary English phonetic rules wherein the two strings on eachline are phonetically interchangeable “a” “ei” “a” “ey” “ai” “ie” “air”“ear” “air” “ere” “air” “are” “are” “ear” “are” “eir” “are” “air” “cc”“k” “ch” “te” “ch” “ti” “ch” “k” “ch” “tu” “ch” “s” “ci” “s” “ear” “air”“ear” “are” “ear” “ere” “ear” “ier” “eau” “o” “ee” “i” “ei” “a” “eir”“are” “eir” “ere” “ere” “ear” “ere” “air” “ere” “eir” “ew” “oo” “ew”“ue” “ew” “u” “ew” “o” “ew” “ui” “ey” “a” “f” “ph” “f” “gh” “ge” “j”“gg” “j” “gh” “f” “i” “igh” “i” “ee” “i” “uy” “ie” “ai” “ier” “ear”“ieu” “oo” “ieu” “u” “igh” “i” “j” “ge” “j” “di” “j” “gg” “k” “qu” “k”“cc” “k” “ch” “kw” “qu” “o” “eau” “o” “ew” “oe” “u” “oo” “u” “oo” “ui”“oo” “ew” “oo” “ieu” “ph” “f” “qu” “k” “qu” “w” “s” “ch” “s” “ti” “s”“ci” “shun” “tion” “shun” “sion” “shun” “cion” “ss” “z” “te” “ch” “ti”“s” “tu” “ch” “u” “ieu” “u” “oo” “u” “ew” “u” “oe” “ue” “ew” “uff”“ough” “ui” “ew” “ui” “oo” “uy” “i” “w” “qu” “z” “ss”

Each string in a text entry is replaced with all of the phoneticequivalents of the string. Regular expressions can sometimes beadvantageously employed if multiple phonetic equivalents exist, as inthe example presented above.

The sequentially next five spell-check algorithms fall within the groupof “mistyping” spell-check algorithms. The first of these is the missingcharacter insertion algorithm. Each letter of the alphabet is addedafter each character of the text entry, again, as may be characterizedin a regular expression.

The sequentially next algorithm is the character swapping algorithmwherein each sequential pair of characters in the text entry are swappedwith one another. Thus, the text entry --phan-- would result in thecharacter strings --hpan-- --pahn-- and --phna--. These three stringswould then be the subject of separate searches of the linguistic datasources.

The sequentially next algorithm is the character omission algorithmwherein each character is individually omitted. Thus, the text entry--phan-- would result in the character strings --han-- --pan-- --phn--and --pha--. These four strings would then be the subject of separatesearches of the linguistic data sources.

The sequentially next algorithm is wherein the text is treated as twoseparate words. This can be accomplished, for instance, by inserting a<SPACE> between adjacent letter or, for instance, can be accomplished bysimply searching a first portion and a second portion of the text entryas separate words, i.e., as separate sub-entries. Other ways ofsearching a text entry as two separate words will be apparent.

The sequentially next algorithm, and the final “mistyping” algorithm, isthe character replacement algorithm wherein each character isindividually replaced by the other characters in the alphabet. A regularexpression may result from subjecting the text entry to the algorithm.

The sequentially next algorithm is the spell-check algorithms that arerelated to specific affixation rules, which typically are localespecific. As suggested above, in the German language an s must beaffixed between the two known words kapitan and patent to form thecombination thereof, thus kapitanspatent. Other types of affixationrules will be apparent.

The next and final rules are related to metaphone analysis. The firstrule relates to generation of a metaphone regular expression, and thenidentifying language objects 100 in the linguistic sources that areconsistent with the metaphone regular expression. Four additional andoptional metaphone-related spell-check algorithms, which are describedin greater detail below, relate to metaphone manipulation.

Regarding the first metaphone-related spell-check algorithm, it is notedthat the metaphone regular expression can be formed, as a generalmatter, by deleting from the text input all of the vowel sounds and byreplacing all of the phonetically equivalent character strings with astandard metaphone “key”. For instance, the various character strings“ssia”, “ssio”, “sia”, “sio”, “sh”, “cia”, “sh”, “tio”, “tia”, and “tch”would each be replaced with the metaphone key “X”. The charactersstrings “f”, “v”, and “ph” would each be replaced with the metaphone key“F”. The metaphone regular expression is then created by placing anoptional vowel wild card, which can constitute any number of differentvowel sounds or no vowel sound, between each metaphone key. Searchingusing the metaphone regular expression can produce excellent spell-checkresults, i.e., excellent spell-check language objects 100, but thesearching that is required can consume significant processing resources.As such, the metaphone regular expression spell-check algorithm isadvantageously performed only after the execution of many otherspell-check algorithms that require much less processing resource andwhich resulted in too few spell-check results.

The last four spell-check algorithms are optional and relate tometaphone manipulation and bear some similarity to the character“mistyping” spell-check algorithms described above. More particularly,after the metaphone regular expression has been created, the fourmetaphone manipulation spell-check algorithms relate to manipulation ofthe metaphone keys within the metaphone regular expression.Specifically, and in sequential order, the last four spellcheck-algorithms are a missing metaphone key insertion spell-checkalgorithm, a metaphone key swapping spell-check algorithm, a metaphonekey omission spell-check algorithm, and a metaphone key exchangespell-check algorithm. These all operate in a fashion similar to thoseof the corresponding character-based “mistyping” algorithms mentionedabove, except involving manipulations to the metaphone keys within themetaphone regular expression.

The spell-check process is depicted generally in FIGS. 9A and 9B and isdescribed herein. Processing starts at 602 where the text entry issubjected to the spell-check algorithm related to ignoringcapitalization and/or accenting, and the linguistic data sources aresearched for spell-check language objects 100. Any spell-check languageobjects 100 that are found are added to a list. It is then determined at604 whether or not the quantity of spell-check language objects 100 inthe list has reached the predetermined quantity. If the predeterminedquantity has been reached, processing continues to 606 where thespell-check language objects 100 are output. Processing thereafterreturns to the main process at 204 in FIG. 3A.

On the other hand, if it is determined at 604 that the predeterminedquantity has not been reached, processing continues to 608 where thetext entry is subjected to the spell-check algorithm related to phoneticreplacement, and the linguistic data sources are searched forspell-check language objects 100. Any spell-check language objects 100that are found are added to the list. It is then determined at 612whether or not the quantity of spell-check language objects 100 in thelist has reached the predetermined quantity. If the predeterminedquantity has been reached, processing continues to 606 where thespell-check language objects 100 are output.

Otherwise, processing continues to 616 where the text entry is subjectedto the spell-check algorithm related to missing character insertion, andthe linguistic data sources are searched for spell-check languageobjects 100. Any spell-check language objects 100 that are found areadded to the list. It is then determined at 620 whether or not thequantity of spell-check language objects 100 in the list has reached thepredetermined quantity. If the predetermined quantity has been reached,processing continues to 606 where the spell-check language objects 100are output.

Otherwise, processing continues to 624 where the text entry is subjectedto the spell-check algorithm related to character swapping, and thelinguistic data sources are searched for spell-check language objects100. Any spell-check language objects 100 that are found are added tothe list. It is then determined at 628 whether or not the quantity ofspell-check language objects 100 in the list has reached thepredetermined quantity. If the predetermined quantity has been reached,processing continues to 606 where the spell-check language objects 100are output.

Otherwise, processing continues to 632 where the text entry is subjectedto the spell-check algorithm related to character omission, and thelinguistic data sources are searched for spell-check language objects100. Any spell-check language objects 100 that are found are added tothe list. It is then determined at 636 whether or not the quantity ofspell-check language objects 100 in the list has reached thepredetermined quantity. If the predetermined quantity has been reached,processing continues to 606 where the spell-check language objects 100are output.

Otherwise, processing continues to 640 where the text entry is subjectedto the spell-check algorithm related to treatment of the text entry asseparate words, and the linguistic data sources are searched forspell-check language objects 100. Any spell-check language objects 100that are found are added to the list. It is then determined at 644whether or not the quantity of spell-check language objects 100 in thelist has reached the predetermined quantity. If the predeterminedquantity has been reached, processing continues to 606 where thespell-check language objects 100 are output.

Otherwise, processing continues to 648 where the text entry is subjectedto the spell-check algorithm related to character exchange, and thelinguistic data sources are searched for spell-check language objects100. Any spell-check language objects 100 that are found are added tothe list. It is then determined at 652 whether or not the quantity ofspell-check language objects 100 in the list has reached thepredetermined quantity. If the predetermined quantity has been reached,processing continues to 606 where the spell-check language objects 100are output.

Otherwise, processing continues to 656 where the text entry is subjectedto the spell-check algorithm related to affixation rules, and thelinguistic data sources are searched for spell-check language objects100. Any spell-check language objects 100 that are found are added tothe list. It is then determined at 660 whether or not the quantity ofspell-check language objects 100 in the list has reached thepredetermined quantity. If the predetermined quantity has been reached,processing continues to 606 where the spell-check language objects 100are output.

Otherwise, processing continues to 664 where the text entry is subjectedto the spell-check algorithm related to creation of the metaphoneregular expression, and the linguistic data sources are searched forspell-check language objects 100. Any spell-check language objects 100that are found are added to the list. It is then determined at 668whether or not the quantity of spell-check language objects 100 in thelist has reached the predetermined quantity. If the predeterminedquantity has been reached, processing continues to 606 where thespell-check language objects 100 are output.

Otherwise, processing continues to 672 where the text entry is subjectedto the spell-check algorithm related to missing metaphone key insertion,and the linguistic data sources are searched for spell-check languageobjects 100. Any spell-check language objects 100 that are found areadded to the list. It is then determined at 676 whether or not thequantity of spell-check language objects 100 in the list has reached thepredetermined quantity. If the predetermined quantity has been reached,processing continues to 606 where the spell-check language objects 100are output.

Otherwise, processing continues to 680 where the text entry is subjectedto the spell-check algorithm related to metaphone key swapping, and thelinguistic data sources are searched for spell-check language objects100. Any spell-check language objects 100 that are found are added tothe list. It is then determined at 684 whether or not the quantity ofspell-check language objects 100 in the list has reached thepredetermined quantity. If the predetermined quantity has been reached,processing continues to 606 where the spell-check language objects 100are output.

Otherwise, processing continues to 688 where the text entry is subjectedto the spell-check algorithm related to metaphone key omission, and thelinguistic data sources are searched for spell-check language objects100. Any spell-check language objects 100 that are found are added tothe list. It is then determined at 692 whether or not the quantity ofspell-check language objects 100 in the list has reached thepredetermined quantity. If the predetermined quantity has been reached,processing continues to 606 where the spell-check language objects 100are output.

Otherwise, processing continues to 696 where the text entry is subjectedto the spell-check algorithm related to metaphone key exchange, and thelinguistic data sources are searched for spell-check language objects100. Processing thereafter continues to 606 where the spell-checklanguage objects 100 are output. Processing afterward returns to themain process at 204 in FIG. 3A.

The exemplary embodiment also includes a dynamic autotext feature whichprovides a learning function related to the learning of spelling errorscommonly made and otherwise corrected by the particular user of thehandheld electronic device 4. For instance, and as is depicted generallyin FIG. 10, the user may wish to input the incorrectly-spelledexpression—thier—. The user may have entered the keys 28<TY><GH><UI><ER> pursuant to typing the first four letters thereof. Thedefault output 68 in such a situation would be the character strings“thir”, such as might correspond with the word “third”. A variant 80“thie” might also be output, such as might correspond with “thief”. Anartificial variant 80 “thue” may also be output at a position ofrelatively lower priority.

Upon entry of the fifth keystroke of the incorrectly-spelled expression--thier--, i.e., <ER>, no language object in the generic word list 88,the new words database 92, or in the other data sources 99 correspondswith the text entry. That is, word context has been lost. However,responsive to the loss of such context the spell-check routine isinitiated, as at 602 in FIG. 9A, and it is determined that the correctlyspelled—their—would be a valid spell-check language object 100 for thistext entry.

However, if the user has not previously made and corrected thisparticular spelling error, the resultant output will be such as thatdepicted generally in FIG. 11. Specifically, the artificial variants--thin-- and --thire-- are output at a position of preference withrespect to the spell-check language object 100 --their--. Specifically,--thin-- is the default output 68, and the expression --thire-- and--their-- are output as variants 80, with the spell-check languageobject 100 --their-- being less preferred. Again, the outputting ofartificial variants at a position of preference with respect tospell-check language objects 100 prior to the system learning thespecific spelling error advantageously promotes the entry of new words.

However, once the user has selected the spell-check language object 100--their--, such as with a selection input, the spell-check routinedetects the selection of a less-preferred spell-check language object100 and performs a learning function. Specifically, the spell-checkroutine stores the erroneous text object --thier-- as a reference object47 in the dynamic autotext table 49. The spell-check routine also storesthe correct spelling --their-- as a value object 51 in the dynamicautotext table 49 and associates the reference object 47 and the valueobject 51. As such, and as is depicted generally in FIG. 12, the nexttime the erroneous key input <TY><GH><UI><ER><ER> is entered by theuser, the reference object 47 --thier-- is identified in the dynamicautotext table 49, and the associated value object 51 --their-- isoutput as a default output 68. The artificial variants --thin-- and--thire-- are output as variants 80.

As can be understood in FIGS. 11 and 12, the spell-check routine isadvantageously configured to output spell-check language object 100 inthe same variant component region 64 where prefix objects thatcorresponded with language objects 100 were output, as in FIG. 10. Itthus can be seen that the spell-check routine provides an output that isadvantageously integrated into the disambiguation 22 to provide to theuser interface of the handheld electronic device 4 an overall integratedappearance. The spell-check routine functions and provides spell-checklanguage objects 100 prior to ending of a text entry session, and ratherprovides such spell-check language objects 100 during the entry of atext entry and prior to entry of a delimiter. It is understood that thespell check routine can also function after entry of a text entry, i.e.,after ending of the specific session during which the given text entrywas entered.

While specific embodiments of the disclosed and claimed concept havebeen described in detail, it will be appreciated by those skilled in theart that various modifications and alternatives to those details couldbe developed in light of the overall teachings of the disclosure.Accordingly, the particular arrangements disclosed are meant to beillustrative only and not limiting as to the scope of the disclosed andclaimed concept which is to be given the full breadth of the claimsappended and any and all equivalents thereof.

1.-49. (canceled)
 50. A method of enabling a spell-check operation on ahandheld electronic device having stored for execution thereon a numberof spell-check algorithms, the method comprising: on the handheldelectronic device, subjecting a text entry to a spell-check algorithm toobtain a resultant expression, and generating a list including anylanguage object determined to be inconsistent with the resultantexpression; subjecting the text entry to a first spell-check algorithmrelated to misspelling when a quantity of language objects in the listhas not reached a predetermined quantity; and subsequent to saidsubjecting the text entry to the first spell-check algorithm related tomisspelling, and when the quantity of language objects in the list hasnot reached the predetermined quantity, subjecting the text entry to afirst spell-check algorithm related to a predetermined affixation rule.51. The method of claim 50, further comprising subjecting the text entryto a first spell-check algorithm related to character configuration. 52.The method of claim 51, wherein the first spell-check algorithm relatedto character configuration comprises a first spell-check algorithmrelated to ignoring at least one of capitalization and accenting. 53.The method of claim 50, further comprising: ceasing said subjecting thetext entry to the spell-check algorithm and said generating the listwhen the quantity of language objects in the list reaches thepredetermined quantity.
 54. The method of claim 50, wherein the firstspell-check algorithm related to misspelling comprises a firstspell-check algorithm related to phonetic replacement.
 55. The method ofclaim 50, further comprising, subsequent to said subjecting the textentry to the first spell-check algorithm related to misspelling, andwhen the quantity of language objects in the list has not reached thepredetermined quantity, subjecting the text entry to a first spell-checkalgorithm related to mistyping.
 56. The method of claim 55, furthercomprising employing as the first spell-check algorithm related tomistyping one of: a spell-check algorithm related to missing characterinsertion; a spell-check algorithm related to character swapping; aspell-check algorithm related to character omission; a spell-checkalgorithm related to one of insertion of a <SPACE> and separation of theentry into a pair of separate sub-entries; and a spell-check algorithmrelated to character exchange.
 57. The method of claim 55, furthercomprising employing as the first spell-check algorithm related tomistyping any two of the following in the gross order of: a spell-checkalgorithm related to missing character insertion; a spell-checkalgorithm related to character swapping; a spell-check algorithm relatedto character omission; a spell-check algorithm related to one ofinsertion of a <SPACE> and separation of the entry into a pair ofseparate sub-entries; and a spell-check algorithm related to characterexchange.
 58. The method of claim 50, further comprising, subsequent tosaid subjecting the text entry to the first spell-check algorithmrelated to a predetermined affixation rule, and when the quantity oflanguage objects in the list has not reached the predetermined quantity,subjecting the text entry to a first spell-check algorithm related tometaphone analysis.
 59. The method of claim 58, wherein the firstspell-check algorithm related to metaphone analysis comprises a firstspell-check algorithm related to generation of a metaphone regularexpression.
 60. The method of claim 59, further comprising, subsequentto said subjecting the text entry to the first spell-check algorithmrelated to generation of a metaphone regular expression, and when thequantity of language objects in the list has not reached thepredetermined quantity, subjecting the text entry to a first spell-checkalgorithm related to metaphone manipulation.
 61. The method of claim 60,further comprising employing as the first spell-check algorithm relatedto metaphone manipulation one of: a spell-check algorithm related tomissing metaphone key insertion; a spell-check algorithm related tometaphone key swapping; a spell-check algorithm related to metaphone keyomission; and a spell-check algorithm related to metaphone key exchange.62. The method of claim 60, further comprising employing as the firstspell-check algorithm related to metaphone manipulation any two of thefollowing in the gross order of: a spell-check algorithm related tomissing metaphone key insertion; a spell-check algorithm related tometaphone key swapping; a spell-check algorithm related to metaphone keyomission; and a spell-check algorithm related to metaphone key exchange.63. A handheld electronic device comprising an input apparatus, aprocessor apparatus, and an output apparatus, the input apparatuscomprising a number of input keys, at least two of the input keys havinga number of linguistic elements assigned thereto, at least two of theinput keys each having as the number of linguistic elements assignedthereto a plurality of linguistic elements, the processor apparatuscomprising a processor and a memory having stored therein a plurality ofobjects comprising a plurality of language objects, at least two of thelanguage objects each comprising a number of the linguistic elements,the memory having stored therein a number of routines which, whenexecuted by the processor, cause the handheld electronic device to beadapted to perform operations comprising: subjecting a text entry to aspell-check algorithm to obtain a resultant expression, and generating alist including any language object determined to be consistent with theresultant expression; subjecting the text entry to a first spell-checkalgorithm related to misspelling when a quantity of language objects inthe list has not reached a predetermined quantity; and subsequent tosaid subjecting the text entry to the first spell-check algorithmrelated to misspelling, and when the quantity of language objects in thelist has not reached the predetermined quantity, subjecting the textentry to a first spell-check algorithm related to a predeterminedaffixation rule.
 64. The handheld electronic device of claim 63, whereinthe operations further comprise subjecting the text entry to a firstspell-check algorithm related to character configuration.
 65. Thehandheld electronic device of claim 64, wherein subjecting the textentry to the first spell-check algorithm related to characterconfiguration comprises subjecting the text entry to a first spell-checkalgorithm related to ignoring at least one of capitalization andaccenting.
 66. The handheld electronic device of claim 63, wherein theoperations further comprise ceasing said subjecting the text entry tothe spell-check algorithm and said generating the list when the quantityof language objects in the list reaches the predetermined quantity. 67.The handheld electronic device of claim 63, wherein the operationsfurther comprise, subsequent to said subjecting the text entry to afirst spell-check algorithm related to misspelling, and when thequantity of language objects in the list has not reached thepredetermined quantity, subjecting the text entry to a first spell-checkalgorithm related to mistyping.
 68. The handheld electronic device ofclaim 63, wherein the operations further comprise, subsequent to saidsubjecting the text entry to the first spell-check algorithm related toa predetermined affixation rule, and when the quantity of languageobjects in the list has not reached the predetermined quantity,subjecting the text entry to a first spell-check algorithm related tometaphone analysis.
 69. A method of enabling a spell-check operation ona handheld electronic device having stored for execution thereon anumber of spell-check algorithms, the method comprising: on the handheldelectronic device, subjecting the text entry to a spell-check algorithmto obtain a resultant expression, and generating a list including anylanguage object determined to be inconsistent with the resultantexpression; subjecting the text entry to a first spell-check algorithmrelated to misspelling when the quantity of language objects in the listhas not reached a predetermined quantity; and subsequent to saidsubjecting the text entry to a first spell-check algorithm related tomisspelling, and when the quantity of language objects in the list hasnot reached the predetermined quantity, subjecting the text entry to afirst spell-check algorithm related to metaphone analysis.